Datasets:
Tasks:
Token Classification
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
ArXiv:
Tags:
legal
License:
language: | |
- en | |
license: mit | |
size_categories: | |
- 10K<n<100K | |
task_categories: | |
- token-classification | |
configs: | |
- config_name: default | |
data_files: | |
- split: train | |
path: data/train-* | |
- split: dev | |
path: data/dev-* | |
- split: test | |
path: data/test-* | |
dataset_info: | |
features: | |
- name: annotations | |
list: | |
- name: result | |
list: | |
- name: from_name | |
dtype: string | |
- name: id | |
dtype: string | |
- name: to_name | |
dtype: string | |
- name: type | |
dtype: string | |
- name: value | |
struct: | |
- name: end | |
dtype: int64 | |
- name: labels | |
sequence: string | |
- name: start | |
dtype: int64 | |
- name: text | |
dtype: string | |
- name: meta | |
struct: | |
- name: source | |
dtype: string | |
- name: id | |
dtype: string | |
- name: data | |
struct: | |
- name: text | |
dtype: string | |
splits: | |
- name: train | |
num_bytes: 7672312 | |
num_examples: 10995 | |
- name: dev | |
num_bytes: 815588 | |
num_examples: 1074 | |
- name: test | |
num_bytes: 3376945 | |
num_examples: 4501 | |
download_size: 5441938 | |
dataset_size: 11864845 | |
tags: | |
- legal | |
Dataset for training and evaluating Indian Legal Named Entity Recognition model. | |
# Paper details | |
[Named Entity Recognition in Indian court judgments](https://aclanthology.org/2022.nllp-1.15/) | |
[Arxiv](https://arxiv.org/abs/2211.03442) | |
### Label Scheme | |
<details> | |
<summary>View label scheme (14 labels for 1 components)</summary> | |
| ENTITY | BELONGS TO | | |
| --- | --- | | |
| `LAWYER` | PREAMBLE | | |
| `COURT` | PREAMBLE, JUDGEMENT | | |
| `JUDGE` | PREAMBLE, JUDGEMENT | | |
| `PETITIONER` | PREAMBLE, JUDGEMENT | | |
| `RESPONDENT` | PREAMBLE, JUDGEMENT | | |
| `CASE_NUMBER` | JUDGEMENT | | |
| `GPE` | JUDGEMENT | | |
| `DATE` | JUDGEMENT | | |
| `ORG` | JUDGEMENT | | |
| `STATUTE` | JUDGEMENT | | |
| `WITNESS` | JUDGEMENT | | |
| `PRECEDENT` | JUDGEMENT | | |
| `PROVISION` | JUDGEMENT | | |
| `OTHER_PERSON` | JUDGEMENT | | |
</details> | |
## Author - Publication | |
``` | |
@inproceedings{kalamkar-etal-2022-named, | |
title = "Named Entity Recognition in {I}ndian court judgments", | |
author = "Kalamkar, Prathamesh and | |
Agarwal, Astha and | |
Tiwari, Aman and | |
Gupta, Smita and | |
Karn, Saurabh and | |
Raghavan, Vivek", | |
booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2022", | |
month = dec, | |
year = "2022", | |
address = "Abu Dhabi, United Arab Emirates (Hybrid)", | |
publisher = "Association for Computational Linguistics", | |
url = "https://aclanthology.org/2022.nllp-1.15", | |
doi = "10.18653/v1/2022.nllp-1.15", | |
pages = "184--193", | |
abstract = "Identification of named entities from legal texts is an essential building block for developing other legal Artificial Intelligence applications. Named Entities in legal texts are slightly different and more fine-grained than commonly used named entities like Person, Organization, Location etc. In this paper, we introduce a new corpus of 46545 annotated legal named entities mapped to 14 legal entity types. The Baseline model for extracting legal named entities from judgment text is also developed.", | |
} | |
``` |